ShuffleNet-V2#
eqxvision.models.ShuffleNetV2
#
A simple port of torchvision.models.shufflenetv2
__init__(self, stages_repeats: List[int], stages_out_channels: List[int], num_classes: int = 1000, inverted_residual: eqx.Module = <class 'eqxvision.models.classification.shufflenetv2._InvertedResidual'>, *, key: Optional[jax.random.PRNGKey] = None)
#
Arguments:
- stages_repeats: Number of times a block is repeated for each stage
- stages_out_channels: Output at each stage
- num_classes: Number of classes in the classification task.
Also controls the final output shape
(num_classes,)
. Defaults to1000
- inverted_residual: Network structure
- key: A
jax.random.PRNGKey
used to provide randomness for parameter initialisation. (Keyword only argument.)
__call__(self, x, *, key: Optional[jax.random.PRNGKey] = None) -> Array
#
Arguments:
x
: The inputJAX
arraykey
: Required parameter. Utilised by few layers such asDropout
orDropPath
eqxvision.models.shufflenet_v2_x0_5(torch_weights: str = None, **kwargs: Any) -> ShuffleNetV2
#
Constructs a ShuffleNetV2 with 0.5x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.shufflenet_v2_x1_0(torch_weights: str = None, **kwargs: Any) -> ShuffleNetV2
#
Constructs a ShuffleNetV2 with 1.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.shufflenet_v2_x1_5(torch_weights: str = None, **kwargs: Any) -> ShuffleNetV2
#
Constructs a ShuffleNetV2 with 1.5x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone
eqxvision.models.shufflenet_v2_x2_0(torch_weights: str = None, **kwargs: Any) -> ShuffleNetV2
#
Constructs a ShuffleNetV2 with 2.0x output channels, as described in ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design.
Arguments:
torch_weights
: APath
orURL
for thePyTorch
weights. Defaults toNone